This is the command msvmocas that can be run in the OnWorks free hosting provider using one of our multiple free online workstations such as Ubuntu Online, Fedora Online, Windows online emulator or MAC OS online emulator
PROGRAM:
NAME
msvmocas - train a multi-class linear SVM classifier
SYNOPSIS
msvmocas [options] example_file model_file
DESCRIPTION
msvmocas is a program that trains a multi-class linear SVM classifier using the Optimized
Cutting Plane Algorithm for Support Vector Machines (OCAS) and produces a model file.
example_file is a file with training examples in SVM^light format, and model_file is the
file in which to store the learned linear rule f(x)=W'*x. model_file contains M columns
and D lines, where M is the number of classes and D the number of dimensions,
corresponding to the elements of the matrix W [D x M].
OPTIONS
A summary of options is included below.
General options:
-h Show summary of options.
-v (0|1)
Set the verbosity level (default: 1)
Learning options:
-c float
Regularization constant C. (default: 1)
-n integer
Use only the first integer examples for training. By default, integer equals the
number of examples in example_file.
Optimization options:
-m (0|1)
Solver to be used:
0 ... standard cutting plane (equivalent to BMRM, SVM^perf)
1 ... OCAS (default)
-s integer
Cache size for cutting planes. (default: 2000)
Stopping conditions:
-a float
Absolute tolerance TolAbs: halt if QP-QD <= TolAbs. (default: 0)
-r float
Relative tolerance TolAbs: halt if QP-QD <= abs(QP)*TolRel. (default: 0.01)
-q float
Desired objective value QPValue: halt is QP <= QPValue. (default: 0)
-t float
Halts if the solver time (loading time is not counted) exceeds the time given in
seconds. (default: infinity)
EXAMPLES
Train the multi-class SVM classifier from example file example4_train.light, with the
regularization constant C=10, verbosity switched off, and save model to msvmocas.model:
msvmocas -c 10 -v 0 example4_train.light msvmocas.model
Compute the testing error of the classifier stored in msvmocas.model with linclassif(1)
using testing examples from example4_test.light and save the predicted labels to
example4_test.pred:
linclassif -e -o example4_test.pred example4_test.light msvmocas.model
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